Abstract

Long-term monitoring is needed to understand the statuses and trends of wildlife communities in montane forests, such as those in Yushan National Park (YSNP), Taiwan. Integrating passive acoustic monitoring (PAM) with an automated sound identifier, a long-term biodiversity monitoring project containing six PAM stations, was launched in YSNP in January 2020 and is currently ongoing. SILIC, an automated wildlife sound identification model, was used to extract sounds and species information from the recordings collected. Animal vocal activity can reflect their breeding status, behaviour, population, movement and distribution, which may be affected by factors, such as habitat loss, climate change and human activity. This massive amount of wildlife vocalisation dataset can provide essential information for the National Park's headquarters on resource management and decision-making. It can also be valuable for those studying the effects of climate change on animal distribution and behaviour at a regional or global scale.
To our best knowledge, this is the first open-access dataset with species occurrence data extracted from sounds in soundscape recordings by artificial intelligence. We obtained seven bird species for the first release, with more bird species and other taxa, such as mammals and frogs, to be updated annually. Raw recordings containing over 1.7 million one-minute recordings collected between the years 2020 and 2021 were analysed and SILIC identified 6,243,820 vocalisations of seven bird species in 439,275 recordings. The automatic detection had a precision of 0.95 and the recall ranged from 0.48 to 0.80. In terms of the balance between precision and recall, we prioritised increasing precision over recall in order to minimise false positive detections. In this dataset, we summarised the count of vocalisations detected per sound class per recording which resulted in 802,670 occurrence records. Unlike data from traditional human observation methods, the number of observations in the Darwin Core organismQuantity column refers to the number of vocalisations detected for a specific bird species and cannot be directly linked to the number of individuals.
We expect our dataset will be able to help fill the data gaps of fine-scale avian temporal activity patterns in montane forests and contribute to studies concerning the impacts of climate change on montane forest ecosystems on regional or global scales.

Addeddate

2024-09-09 09:29:12

Bhl_virtual_titleid

210877

Bhl_virtual_volume

v.11 (2023)

Call number

10_3897_BDJ_11_e97811

Call-number

10_3897_BDJ_11_e97811

Foldoutcount

0

Genre

article

Identifier

acousticdetecti11wush

Identifier-ark

ark:/13960/s28w27x60dt

Identifier-bib

10_3897_BDJ_11_e97811

Identifier-doi

10.3897/BDJ.11.e97811

Identifier-elocator

e97811

Ocr

tesseract 5.3.0-6-g76ae

Ocr_detected_lang

en

Ocr_detected_lang_conf

1.0000

Ocr_detected_script

Latin

Ocr_detected_script_conf

1.0000

Ocr_module_version

0.0.21

Ocr_parameters

-l eng

Page_number_confidence

77

Page_number_module_version

1.0.5

Page_range

1-15

Pages

15

Pdf_degraded

invalid-jp2-headers

Pdf_module_version

0.0.25

Possible copyright status

In copyright. Digitized with the permission of the rights holder.

Ppi

300

Source

Biodiversity Data Journal 11

Year

2023

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